1 + 1 = 3 → Combining crowd based maintenance with repositioning for operational excellence
Continuing our series of articles on our StreetCrowd service, we are going to explore how the crowd can be utilized to offer services beyond fleet repositioning. In particular, the way in which data-driven clustering of tasks for the crowd can improve operational efficiency for car sharing and micromobility operators. This includes tasks like cleaning, charging & repairs.
Having a fleet that is in a good condition which in turn provides users with a safe and comfortable experience, should be paramount for mobility as a service operator (MaaS). This is something that will become ever more important with the emergence of shared autonomous vehicle fleets (AVs) and the decline of vehicle ownership. By combining fleet repositioning with other tasks such as maintenance, the true potential of the ‘crowd’ can be realised.
Previously we explored how demand prediction and the incentivization of crowd users to reposition vehicles, can help to increase utilization by increasing the amount of time vehicles are in use. They do this by repositioning vehicles into the right place at the right time. However, it isn’t simply about location. To maintain high operational efficiency, operators must make sure that vehicles are maintained to a good standard. Nobody wants to get into a dirty car or have to wade through piles of rubbish. Operators can achieve these efficiencies through predictive fleet management and as we will show: utilization of the ‘crowd’.
Predictive fleet management is important because overall utilization would be compromised if vehicles were to be cleaned or repaired when they could be in use. Data from mobility operators shows that profitability doubles for a service operating at a 30% utilization rate, so this is no small matter (Ridecell). A data-driven approach to assess when a vehicle needs cleaning can be based on the number of rides or by using sensors which can recognise untidiness. Once this is established, it is important that the vehicles are cleaned at times when demand is low as not to waste precious revenue opportunities.
Keep it clean
Cleaning is a good example and it is something that should not be overlooked. Cleanride — a company based in Berlin and focused on using AI to identify dirt and mess — investigated the state of the ridesharing cars in the city. They found that 38% of the 85 car interiors they tested, would be considered unpleasant, messy or very dirty. With images of cars in bad condition able to spread fast across social media, it is important to get on top of this as to avoid reputational damage.
Traditionally, fleet operators have relied on contractors that clean fleets of vehicles at a central location. However, in our endeavour to build and utilize ‘crowds’, we see a better and more efficient way. What if the drivers themselves could be incentivized to clean the cars? Crowd members could be equipped with free car wash vouchers and given extra credit to bring dirty cars they use to a car wash. This would be far more efficient than hiring operators, as users can choose to wash the car en route.
Where the potential for ‘crowd’ can truly be realised is by combining operational efforts such as repositioning and cleaning into one task. Users who may already be tasked with moving vehicles into other parts of the city, can at the same time be encouraged to carry out maintenance tasks. In doing so, operators can the impetus on the crowd to take control of the operational side of things.
Autonomous vehicles and ownership
The issue of operational efficiency will become even more important when we look at the onset of autonomous vehicles. Currently, the job of cleaning and repairing cars is left to the drivers themselves, be it for private use or for ridesharing. These human drivers know when to clean their vehicles and make the decision to do so when it is appropriate.
Future AV fleets will require a complete rethink of how this is done. Much more emphasis will need to be put on providing an efficient operational strategy, as ownership decreases. Because of this, car sharing operators who can get this right now will be much better placed to adapt to the maintenance demands of robo-taxi business models.
Utilizing the crowd for maintenance is about many things. On the one hand, operators benefit from greater efficiency and lower costs. They are able to get more of the vehicles on the road and can establish a reputation for reliability. On the other hand, the users themselves benefit not just from a more pleasant experience, but also from the opportunities that being part of the “crowd” brings. By trading their time for money they can benefit from the mobility ecosystem in different ways. Data-driven insights are the basis to cluster operational tasks where this makes sense.
At Ubiq, we are experts at providing demand predictions for shared mobility and combine it with the execution power of StreetCrowd. It works complementary to any existing service operation.